Department of Applied Mathematics and Analysis, University of Barcelona, Barcelona, Spain.
Med Image Anal. 2012 Aug;16(6):1085-100. doi: 10.1016/j.media.2012.06.008. Epub 2012 Jul 23.
We present a fully automatic methodology for the detection of the Media-Adventitia border (MAb) in human coronary artery in Intravascular Ultrasound (IVUS) images. A robust border detection is achieved by means of a holistic interpretation of the detection problem where the target object, i.e. the media layer, is considered as part of the whole vessel in the image and all the relationships between tissues are learnt. A fairly general framework exploiting multi-class tissue characterization as well as contextual information on the morphology and the appearance of the tissues is presented. The methodology is (i) validated through an exhaustive comparison with both Inter-observer variability on two challenging databases and (ii) compared with state-of-the-art methods for the detection of the MAb in IVUS. The obtained averaged values for the mean radial distance and the percentage of area difference are 0.211 mm and 10.1%, respectively. The applicability of the proposed methodology to clinical practice is also discussed.
我们提出了一种全自动的方法来检测人体冠状动脉中的血管中层-外膜边界（MAb）在血管内超声（IVUS）图像中。通过对检测问题的整体解释，实现了稳健的边界检测，其中目标对象，即中膜层，被视为图像中整个血管的一部分，并且学习了所有组织之间的关系。提出了一种相当通用的框架，利用多类组织特征以及关于组织形态和外观的上下文信息。该方法（i）通过与两个具有挑战性的数据库中的观察者间变异性进行详尽比较，以及（ii）与用于检测 IVUS 中 MAb 的最先进方法进行比较，进行了验证。分别获得的平均径向距离和面积差异百分比的平均值为 0.211 毫米和 10.1%。还讨论了所提出的方法在临床实践中的适用性。
